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Diehl, Frederik (2019) Windfarm detection based on Sentinel-1 imagery and deep learning techniques. Master's, Universität Trier.
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| Item URL in elib: | https://elib.dlr.de/123868/ | ||||||||
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| Document Type: | Thesis (Master's) | ||||||||
| Title: | Windfarm detection based on Sentinel-1 imagery and deep learning techniques | ||||||||
| Authors: |
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| Date: | 2019 | ||||||||
| Refereed publication: | No | ||||||||
| Open Access: | No | ||||||||
| Number of Pages: | 98 | ||||||||
| Status: | Published | ||||||||
| Keywords: | Deep learning, Sentinel-1, wind farm detection, coastal application | ||||||||
| Institution: | Universität Trier | ||||||||
| Department: | Umweltfernerkundung und Geoinformatik | ||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||
| HGF - Program: | Space | ||||||||
| HGF - Program Themes: | Earth Observation | ||||||||
| DLR - Research area: | Raumfahrt | ||||||||
| DLR - Program: | R EO - Earth Observation | ||||||||
| DLR - Research theme (Project): | R - Remote Sensing and Geo Research | ||||||||
| Location: | Oberpfaffenhofen | ||||||||
| Institutes and Institutions: | German Remote Sensing Data Center > Land Surface Dynamics | ||||||||
| Deposited By: | Huth, Juliane | ||||||||
| Deposited On: | 26 Nov 2019 12:14 | ||||||||
| Last Modified: | 26 Nov 2019 12:14 |
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